AI companion apps, personalized executive assistants, adaptive tutoring platforms, and B2B account-management agents that need to remember user preferences, past decisions, and evolving facts across months of conversations frequently search to hire a freelance long-term AI agent memory (Mem0, Zep, LangMem, Letta/MemGPT & custom `pgvector` + Graph memory) programmer. I am Aldian Fazrihady, a Senior Freelance AI Application Engineer.
Core Episodic, Semantic & Procedural AI Memory Capabilities
- Three-Tier Cognitive Memory Architecture: Separating short-term sliding working memory, episodic conversation summaries, and long-term semantic user/entity fact graphs stored in PostgreSQL `pgvector` and Neo4j/Memgraph.
- Temporal Fact Resolution & Contradiction Consolidation: Automatically superseding outdated memories (e.g., when a user changes roles, cities, or project tech stacks) using asynchronous background memory-consolidation workers with `valid_from` / `valid_until` timestamps.
- Sub-30ms Memory Injection & Privacy Controls: Retrieving top-k relevant user memories in parallel before LLM prompt assembly, paired with a transparent UI where users can inspect or delete stored memories (GDPR compliance).
Frequently Asked Questions
How do you prevent an AI agent's long-term memory from accumulating contradictory or stale facts over time?
When extracting new candidate facts after a conversation turn, our memory worker retrieves existing semantically similar facts for that user and runs a lightweight conflict-resolution step (`ADD`, `UPDATE/SUPERSEDE`, or `NOOP`).
How do I hire you for stateful AI agent memory engineering?
Hire me via Upwork.